{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('fivethirtyeight')\n",
    "plt.gray()\n",
    "import mglearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score:0.99\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "cancer = load_breast_cancer()\n",
    "X_train,X_test, y_train, y_test = train_test_split(cancer.data, cancer.target, random_state=1026)\n",
    "\n",
    "scaler = MinMaxScaler().fit(X_train)\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "\n",
    "svm  = SVC()\n",
    "svm.fit(X_train_scaled,y_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "print('Test score:{:.2f}'.format(svm.score(X_test_scaled, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best cross validation accuracy:0.97\n",
      "Best set score:0.99\n",
      "Best parameters:  {'C': 100, 'gamma': 0.1}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "param_grid = {'C':[0.001,0.01,0.1,1,10,100], 'gamma':[0.001,0.01,0.1,1,10,100]}\n",
    "grid = GridSearchCV(SVC(), param_grid=param_grid, cv=5)\n",
    "grid.fit(X_train_scaled,y_train)\n",
    "\n",
    "print('Best cross validation accuracy:{:.2f}'.format(grid.best_score_))\n",
    "print('Best set score:{:.2f}'.format(grid.score(X_test_scaled,y_test)))\n",
    "print('Best parameters: ', grid.best_params_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_improper_processing()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score:0.99\n"
     ]
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "pipe = Pipeline([('scaler', MinMaxScaler()),('svm', SVC())])\n",
    "pipe.fit(X_train, y_train)\n",
    "print('Test score:{:.2f}'.format(pipe.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best cross validation accuracy:0.97\n",
      "Test score: 0.99\n",
      "Best parameters:{'svm__C': 10, 'svm__gamma': 0.1}\n"
     ]
    }
   ],
   "source": [
    "param_grid = {'svm__C':[0.001, 0.01, 0.1, 1, 10, 100],'svm__gamma':[0.001, 0.01, 0.1, 1, 10, 100]}\n",
    "grid = GridSearchCV(pipe, param_grid=param_grid, cv=8)\n",
    "grid.fit(X_train ,y_train)\n",
    "\n",
    "print('Best cross validation accuracy:{:.2f}'.format(grid.best_score_))\n",
    "print('Test score: {:.2f}'.format(grid.score(X_test, y_test)))\n",
    "print('Best parameters:{}'.format(grid.best_params_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_proper_processing()"
   ]
  }
 ],
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